Extra: Venue 7 electrics

If you see a straight line, it’s interpolated missing data. Try the sliders to see detail!

import plotly.graph_objects as go
import numpy as np
import pandas as pd

df = pd.read_csv("venue-7-clampon-data.csv")
df["timestamp"] = pd.to_datetime(df['created_at'])
df = df.fillna(value=0)

phase1trace = go.Scatter(customdata=df, 
                    y=df['field1'], 
                    x = df['timestamp'], 
                    mode='lines', 
                    hoverinfo='all', 
                    name='phase 1',
                    )

phase2trace = go.Scatter(customdata=df, 
                    y=df['field2'], 
                    x = df['timestamp'], 
                    mode='lines', 
                    hoverinfo='all', 
                    name='phase 2',
                    )
phase3trace = go.Scatter(customdata=df, 
                    y=df['field3'], 
                    x = df['timestamp'], 
                    mode='lines', 
                    hoverinfo='all', 
                    name='phase 3',
                    )

g = go.FigureWidget(data=[phase1trace,phase2trace,phase3trace])
g.layout.title = 'CurrentCost clamp-on meter readings'
g.layout.xaxis.title= 'timestamp'
g.layout.yaxis.title = "Watts"
g.layout.width = 1000
g.layout.height = 500

fig = go.Figure(g)

fig.update_layout(
    hovermode='x unified',
    hoverlabel=dict(
        bgcolor="white",
        # font_size=16,
        font_family="Rockwell"
    )
)

# Add range slider
fig.update_layout(
    xaxis=dict(
        rangeselector=dict(
            buttons=list([
                dict(
                     label="All",
                     step="all"
                     ),
                                dict(count=1,
                     label="Hour",
                     step="hour",
                     stepmode="todate"),
                dict(count=1,
                     label="Day",
                     step="day",
                     stepmode="backward"),
                dict(count=7,
                     label="Week",
                     step="day",
                     stepmode="backward"),
                dict(count=1,
                     label="Year",
                     step="year",
                     stepmode="backward")
            ])
        ),
        rangeslider=dict(
            visible=True,
        ),
        type="date"
    )
)


# fig.update_yaxes(range=[50, 60])  



# fig.update_yaxes(range = [-5, df['temperature'].max()+5])

fig.show()

# second figure 

sumtrace = go.Scatter(customdata=df, 
                    y=df['field1'] + df['field2'] + df['field3'], 
                    x = df['timestamp'], 
                    mode='lines', 
                    hoverinfo='all', 
                    name='sum of three phases',
                    )

g2 = go.FigureWidget(data=[sumtrace])
g2.layout.title = 'CurrentCost clamp-on meter readings - simple sum of phases'
g2.layout.xaxis.title= 'timestamp'
g2.layout.yaxis.title = "Watts"
g2.layout.width = 1000
g2.layout.height = 500

fig2 = go.Figure(g2)

fig2.update_layout(
    hovermode='x unified',
    hoverlabel=dict(
        bgcolor="white",
        # font_size=16,
        font_family="Rockwell"
    )
)

# Add range slider
fig2.update_layout(
    xaxis=dict(
        rangeselector=dict(
            buttons=list([
                dict(
                     label="All",
                     step="all"
                     ),
                                dict(count=1,
                     label="Hour",
                     step="hour",
                     stepmode="todate"),
                dict(count=1,
                     label="Day",
                     step="day",
                     stepmode="backward"),
                dict(count=7,
                     label="Week",
                     step="day",
                     stepmode="backward"),
                dict(count=1,
                     label="Year",
                     step="year",
                     stepmode="backward")
            ])
        ),
        rangeslider=dict(
            visible=True,
        ),
        type="date"
    )
)


fig2.show()

It’s complicated to know how much electricity is drawn from the three phases of a meter - it’s not just a case of adding the three phases up. But this will give some idea.

This plot is interesting because it shows a minimum of around 2 kW base load with lots of 2.5 kW spikes even when the building is unoccupied. If that electricity isn’t doing useful work, getting those under control could substantially cut the electricity bill. The equipment we are using isn’t very common and we only have one set, but getting a smart meter is another way of seeing this kind of information. Admittedly, with half hourly readings a smart meter isn’t nearly as informative. This is roughly what the same data would look like with a smart meter.

# downsample to every 30 minutes

df.field1 = df.field1.astype(int)
df.field2 = df.field2.astype(int) 
df.field3 = df.field3.astype(int)

# aggregate over 30 minute intervals - this is the interval for smart meter readings.
s = df.resample('30T', on='timestamp', origin='start').agg({'field1':'mean','field2':'mean','field3':'mean'})
s['timestamp'] = s.index 
s = s.fillna(value=0)

downsampled1 = go.Scatter(customdata=s, 
                    y=s['field1']/1000, 
                    x = s['timestamp'], 
                    mode='lines', 
                    hoverinfo='all', 
                    name='phase 1',
                    )
downsampled2 = go.Scatter(customdata=s, 
                    y=s['field2']/1000, 
                    x = s['timestamp'], 
                    mode='lines', 
                    hoverinfo='all', 
                    name='phase 2',
                    )
downsampled3 = go.Scatter(customdata=s, 
                    y=s['field3']/1000, 
                    x = s['timestamp'], 
                    mode='lines', 
                    hoverinfo='all', 
                    name='phase 3',
                    )

g3 = go.FigureWidget(data=[downsampled1, downsampled2, downsampled3])
g3.layout.title = 'CurrentCost clamp-on meter readings - aggregated for half hour slots'
g3.layout.xaxis.title= 'timestamp'
g3.layout.yaxis.title = "kW"
g3.layout.width = 1000
g3.layout.height = 500

fig3 = go.Figure(g3)

fig3.update_layout(
    hovermode='x unified',
    hoverlabel=dict(
        bgcolor="white",
        # font_size=16,
        font_family="Rockwell"
    )
)

# Add range slider
fig3.update_layout(
    xaxis=dict(
        rangeselector=dict(
            buttons=list([
                dict(
                     label="All",
                     step="all"
                     ),
                                dict(count=1,
                     label="Hour",
                     step="hour",
                     stepmode="todate"),
                dict(count=1,
                     label="Day",
                     step="day",
                     stepmode="backward"),
                dict(count=7,
                     label="Week",
                     step="day",
                     stepmode="backward"),
                dict(count=1,
                     label="Year",
                     step="year",
                     stepmode="backward")
            ])
        ),
        rangeslider=dict(
            visible=True,
        ),
        type="date"
    )
)


fig3.show()